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<p>The large volume and complexity of medical imaging datasets pose significant challenges for storage, transmission, and processing. To address these issues, low-rank matrix approximation (LORMA) and its derivative, local LORMA (LLORMA), have shown promising potential. This paper presents a comprehensive literature review of the application of LORMA and LLORMA across various imaging modalities and examines the challenges and limitations of existing methods. Notably, since 2015, there has been a significant shift toward a preference for LLORMA in the medical imaging field, demonstrating its effectiveness in capturing complex structures in medical data compared to LORMA. Given the limitations of shallow similarity methods in LLORMA, we propose incorporating advanced semantic image segmentation to improve the accuracy of similarity measurement. We further explain how this approach can be utilized to identify similar patches and assess its feasibility in medical imaging applications. We observe that LORMA and LLORMA have primarily been applied to unstructured medical data, and we suggest extending their use to other types of medical data, including structured and semi-structured formats. This paper also explores how LORMA and LLORMA can be adapted for regular data with missing entries, considering the impact of inaccuracies in predicting these missing values and their consequences. In addition, we examine the effect of patch size and suggest using random search (RS) to identify the optimal patch size. To further enhance feasibility, we propose a hybrid approach combining Bayesian optimization and RS, which could improve the application of LORMA and LLORMA in medical imaging.</p>

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A systematic review of low-rank and local low-rank matrix approximation in big data medical imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月4日

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  • Sisipho Hamlomo,
  • Marcellin Atemkeng,
  • Yusuf Brima,
  • Chuneeta Nunhokee,
  • Jeremy Baxter

摘要

The large volume and complexity of medical imaging datasets pose significant challenges for storage, transmission, and processing. To address these issues, low-rank matrix approximation (LORMA) and its derivative, local LORMA (LLORMA), have shown promising potential. This paper presents a comprehensive literature review of the application of LORMA and LLORMA across various imaging modalities and examines the challenges and limitations of existing methods. Notably, since 2015, there has been a significant shift toward a preference for LLORMA in the medical imaging field, demonstrating its effectiveness in capturing complex structures in medical data compared to LORMA. Given the limitations of shallow similarity methods in LLORMA, we propose incorporating advanced semantic image segmentation to improve the accuracy of similarity measurement. We further explain how this approach can be utilized to identify similar patches and assess its feasibility in medical imaging applications. We observe that LORMA and LLORMA have primarily been applied to unstructured medical data, and we suggest extending their use to other types of medical data, including structured and semi-structured formats. This paper also explores how LORMA and LLORMA can be adapted for regular data with missing entries, considering the impact of inaccuracies in predicting these missing values and their consequences. In addition, we examine the effect of patch size and suggest using random search (RS) to identify the optimal patch size. To further enhance feasibility, we propose a hybrid approach combining Bayesian optimization and RS, which could improve the application of LORMA and LLORMA in medical imaging.